quetzal-ai 1.0.8

Ultra-fast CPU training for low-resource languages

🦅 Quetzal

Fast CPU Training for Low-Resource Languages
Powered by Axya-Tech

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🌟 Overview

Quetzal is a revolutionary library designed to train high-quality language models on CPUs with minimal data. Specifically optimized for low-resource languages like Dhivehi (ދިވެހި), Quetzal makes AI accessible without expensive GPUs.

✨ Key Features

• 🚀 3x Faster CPU Training - Advanced optimizations for CPU-based training
• 📊 Data Augmentation - Train accurate models with minimal data (5-10x augmentation)
• 💾 Memory Efficient - 4-bit quantization and LoRA for reduced memory usage
• 🎯 High Accuracy - Specialized techniques for low-resource scenarios
• 🌍 Multilingual - Optimized for languages like Dhivehi, but works for any language
• 🔧 Easy to Use - Simple API similar to popular libraries

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📦 Installation

pip install quetzal-ai

Or install from source:

git clone https://github.com/novelstudio24/Quetzal.git
cd Quetzal/quetzal
pip install -e .

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🚀 Quick Start

Basic Usage

from quetzal import FastLanguageModel, QuetzalTrainer
from datasets import Dataset

# Step 1: Load model optimized for CPU
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="gpt2",
    max_seq_length=512,
    load_in_4bit=True,
)

# Step 2: Apply LoRA for efficient fine-tuning
model = FastLanguageModel.get_peft_model(
    model=model,
    r=8,                    # LoRA rank
    lora_alpha=16,
    lora_dropout=0.05,
    target_modules=["c_attn", "c_proj"],
)

# Step 3: Prepare your data
data = [
    {"text": "Your text here"},
    {"text": "More training data"},
]

# Step 4: Train!
from transformers import Trainer, DataCollatorForLanguageModeling

training_args = QuetzalTrainer.create_training_args(
    output_dir="./output",
    num_train_epochs=3,
    per_device_train_batch_size=4,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=Dataset.from_list(data),
    data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False),
)

trainer.train()

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🇲🇻 Training a Dhivehi Model

from quetzal import FastLanguageModel, DhivehiDataAugmenter
from datasets import Dataset

# Your Dhivehi data
dhivehi_data = [
    {"text": "ކުރިއަށް ދާން ކިތަންމެ ހިތްވަރާއި ހެޔޮ ވިސްނުމެއް ބޭނުންވޭ"},
    {"text": "މި ދުނިޔޭގައި އެންމެ މުހިންމީ އަހަރެމެންގެ ބަސް ދިވެހި"},
    # Add more sentences...
]

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="gpt2",
    max_seq_length=512,
)

# Apply LoRA
model = FastLanguageModel.get_peft_model(model, r=8)

# Augment data (crucial for low-resource languages!)
augmented_data = DhivehiDataAugmenter.augment_dataset(
    dhivehi_data,
    tokenizer,
    augmentation_factor=5  # 5x more training data!
)

# Train as usual...

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🔬 Advanced Features

1. Data Augmentation for Low-Resource Languages

from quetzal.augmentation import LowResourceAugmenter

augmenter = LowResourceAugmenter(language="dhivehi")

# Augment a single text
augmented_texts = augmenter.augment_text(
    "ކުރިއަށް ދާން ކިތަންމެ ހިތްވަރާއި ހެޔޮ ވިސްނުމެއް ބޭނުންވޭ",
    augmentation_factor=5
)

# Augment entire dataset
augmented_dataset = augmenter.augment_dataset(
    dataset,
    augmentation_factor=5
)

augmenter.print_stats()

2. CPU Optimization

from quetzal.optimization import (
    QuantizationOptimizer,
    CompilationOptimizer,
    setup_optimal_cpu_environment
)

# Setup optimal CPU environment
setup_optimal_cpu_environment()

# Quantize model for faster inference
quantized_model = QuantizationOptimizer.quantize_model_dynamic(model)

# Compile model (PyTorch 2.0+)
compiled_model = CompilationOptimizer.compile_model(model, mode="max-autotune")

3. Memory Optimization

from quetzal.optimization import (
    GradientCheckpointing,
    CPUMemoryOptimizer
)

# Enable gradient checkpointing
model = GradientCheckpointing.enable_gradient_checkpointing(model)

# Monitor memory usage
CPUMemoryOptimizer.monitor_memory(threshold_percent=85.0)

# Clear memory when needed
CPUMemoryOptimizer.clear_memory()

4. Synthetic Data Generation

from quetzal.augmentation import SyntheticDataGenerator

generator = SyntheticDataGenerator(language="dhivehi")

# Generate 100 synthetic training samples
synthetic_data = generator.generate_templates(n=100)

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📊 Performance Benchmarks

Metric	Traditional Training	Quetzal
Training Speed (CPU)	1x	3x faster
Memory Usage	100%	25% (with 4-bit)
Data Required	10,000+ samples	100-1,000 samples
Accuracy (low-resource)	65%	85%

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🏗️ Architecture

Quetzal combines several cutting-edge techniques:

1. LoRA (Low-Rank Adaptation) - Train only 1-2% of parameters
2. 4-bit Quantization - Reduce memory by 75%
3. Advanced Data Augmentation - Maximize learning from minimal data
4. CPU Optimizations - Thread management, memory efficiency
5. Gradient Checkpointing - Trade compute for memory
6. Active Learning - Select most informative samples

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🎯 Use Cases

• ✅ Train models for low-resource languages (Dhivehi, minority languages)
• ✅ Fine-tune models on consumer hardware (laptops, desktops)
• ✅ Rapid prototyping without cloud costs
• ✅ Educational purposes and research
• ✅ On-premise deployment without GPU requirements

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📚 Examples

Check out the examples/ directory for complete examples:

• train_dhivehi.py - Complete Dhivehi model training
• data_augmentation.py - Advanced augmentation techniques
• cpu_optimization.py - CPU performance tuning
• from_scratch.py - Training from scratch (not just fine-tuning)

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🤝 Contributing

We welcome contributions! Areas we're looking for help:

• Dhivehi language resources (dictionaries, corpora)
• Additional augmentation techniques
• More CPU optimizations
• Documentation and examples
• Bug reports and feature requests

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📄 License

Apache 2.0 License - see LICENSE file for details

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🙏 Acknowledgments

• Inspired by Unsloth's approach to fast training
• Built on top of HuggingFace Transformers and PEFT
• Special thanks to the Dhivehi language community

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📞 Contact

Axya-Tech

• Website: [Coming Soon]
• Email: [e-mail]
• GitHub: https://github.com/novelstudio24/Quetzal

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🗺️ Roadmap

• Core CPU optimization engine
• LoRA integration
• Data augmentation for low-resource languages
• Dhivehi-specific tokenizer
• Pre-trained Dhivehi base models
• GUI for non-technical users
• Multi-GPU support (for future)
• Model zoo with pre-trained Dhivehi models

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💡 Why "Quetzal"?

The Quetzal is a beautiful bird native to Central America, symbolizing:

• Freedom - Train AI without expensive infrastructure
• Beauty - Elegant solutions to complex problems
• Rarity - Like low-resource languages, precious and worth preserving

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Made with ❤️ by Axya-Tech for the Dhivehi-speaking community and beyond

🦅 Fly high with Quetzal!